Proceedings

LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning

Jian Liu, Leyang Cui, Hanmeng Liu, Dandan Huang, Yile Wang, Yue Zhang

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Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. Main track. Pages 3622-3628.

https://doi.org/10.24963/ijcai.2020/501

Keywords

  • Natural Language Processing: Resources and Evaluation
  • Natural Language Processing: Question Answering

Abstract

Machine reading is a fundamental task for testing the capability of natural language understand- ing, which is closely related to human cognition in many aspects. With the rising of deep learning techniques, algorithmic models rival human performances on simple QA, and thus increasingly challenging machine reading datasets have been proposed. Though various challenges such as evidence integration and commonsense knowledge have been integrated, one of the fundamental capabilities in human reading, namely logical reasoning, is not fully investigated. We build a comprehensive dataset, named LogiQA, which is sourced from expert-written questions for testing human Logical reasoning. It consists of 8,678 QA instances, covering multiple types of deductive reasoning. Results show that state-of-the-art neural models perform by far worse than human ceiling. Our dataset can also serve as a benchmark for reinvestigating logical AI under the deep learning NLP setting. The dataset is freely available at https://github.com/lgw863/LogiQA-dataset.

Jian Liu, Leyang Cui, Hanmeng Liu, Dandan Huang, Yile Wang, and Yue Zhang. 2020. LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20. IJCAI, 3622–3628. https://doi.org/10.24963/ijcai.2020/501
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